The mainstream learning-based methods focus on how to build a complex feature extractor, or combine spatial and frequency domain information to distinguish between real and fake faces through dichotomy. Nguyen  [1]} propose a detection method based on capsule network [2]}. Li  [3]} propose that the artifacts in affine face warping as the distinctive feature to forgery detect, and achieve state-of-the-art performance based on SSPNet  [4]}. Li  [5]} propose Face X-ray that focuses on the blending step of forgery and achieve state-of-the-art transferability performance. However, it cannot be used for fully synthesized images, and the performance on low-resolution images drop sharply. Durallet  [6]} first propose the use of Discrete Fourier Transform (DFT) to mine abnormal information in forged face images. Luo  [7]} propose to utilize the high-frequency noises for face forgery detection. Qian  [8]} use DFT to extract frequency-aware image decomposition and local frequency statistics to improve the forgery detection performance on high-compression videos, but the cross-data set performance dropped significantly.
